非结构道路环境下的智能汽车质心侧偏角估计

车辆质心侧偏角是描述车辆侧向运动状态的重要参量之一,其估计的精度直接影响车辆的安全控制,传统的质心侧偏角估计方法不能满足非结构道路环境下的智能汽车质心侧偏角估计的要求。通过建立3自由度智能汽车动力学模型,采用CarSim和MATLAB构建智能汽车整车参数化模型;基于扩展kalman滤波(EKF)算法,设计非结构道路环境下的状态观测器对智能汽车质心侧偏角进行估计。在高、低附着系数路面双移线工况和蛇形工况下,对状态观测器的估计效果进行联合仿真验证。仿真结果表明:该方法能较精确地估计出非结构道路环境下智能汽车的质心侧偏角。     

基于道路摩擦系数估计的智能小车紧急避障策略研究

雾霾环境下驾驶员的视野受到限制,无法准确估计周围的环境信息,对行车安全具有重大影响.自主紧急制动(AEB)系统是一种重要的车辆主动安全功能,用来避免碰撞或减轻碰撞程度.通常,AEB系统利用一个碰撞时间TTC衡量与障碍物发生碰撞的危险程度.通常设计用于制动的TTC门槛值时假设道路摩擦系数为常数,然而,道路情况复杂多变,道路摩擦系数也是变化的,驾驶员在雾霾环境下更难准确估计道路摩擦系数.因此,开发了一个考虑不同摩擦系数对TTC门槛值影响的AEB控制策略.首先用一个复合滑移率轮胎模型来估计峰值道路摩擦系数,再用该系数计算TTC的门槛值,进而利用该Trc门槛值衡量与障碍物发生碰撞的危险程度.因为可以实时识别道路摩擦系数,提出的AEB策略可以自适应雾霾环境下不同的道路表面.仿真结果表明了该方法的有效性.     

多算法结合的汽车参数和状态估计方法研究

为实时准确获取汽车参数及状态信息以提高汽车主动安全性能,提出了一种多算法结合的自适应估计算法。该算法将递推最小二乘算法、蚁群优化算法及容积卡尔曼滤波算法进行有效结合,同时将含有不准确模型参数及未知时变噪声的三自由度非线性整车模型作为标称模型。采用递推最小二乘算法实时估计汽车参数,引入蚁群优化算法实时跟踪容积卡尔曼滤波器的过程噪声及量测噪声,根据目标函数对噪声协方差进行寻优,以解决系统的噪声时变问题,从而获取汽车状态的准确估计。基于CarSim/Simulink的仿真实验结果表明,该算法的状态估计精度高,且具备汽车模型参数校正能力,可以满足系统的控制需要。     

Estimation of road frictional force and wheel slip for effective antilock braking system (ABS) control

The introduction of electric braking via brake‐by‐wire systems in electric vehicles) has reduced the high transportation delays usually involved in conventional friction braking systems. This has facilitated the design of more efficient and advanced control schemes for antilock braking systems (ABSs). However, accurate estimation of the tire‐road friction coefficient, which cannot be measured directly, is required. This paper presents a review of existing estimation methods, focusing on sliding‐mode techniques, followed by the development of a novel friction estimation technique, which is used to design an efficient ABS control system. This is a novel slip‐based estimation method, which accommodates the coupling between the vehicle dynamics, wheel dynamics, and suspension dynamics in a cascaded structure. A higher‐order sliding‐mode observer–based scheme is designed, considering the nonlinear relationship between friction and slip. A first‐order sliding‐mode observer is also designed based on a purely linear relationship. A key feature of the proposed estimation schemes is the inclusion of road slope and the effective radius of the tire as an estimated state. These parameters impact significantly on the accuracy of slip and friction estimation. The performance of the proposed estimation schemes are validated and benchmarked against a Kalman filter (KF) by a series of simulation tests. It is demonstrated that the sliding‐mode observer paradigm is an important tool in developing the next generation ABS systems for electric vehicles.     

太阳能赛车能量流建模仿真和实验研究

为制定太阳能赛车高效能量管理策略,以太阳能赛车为研究对象,建立考虑光伏电池温度、空气密度、胎压、悬架阻尼等时变参数的整车模型,设计了基于带遗忘因子的递推最小二乘法在线道路坡度估计算法,建立了整车MATLAB/Simulink模型,仿真研究了巡航速度、整车质量和轮胎压力参数变化对于整车能耗特性的影响,并进行了实验验证。实验结果表明上述模型能够真实反应车辆和环境参数变化对于整车能耗的影响,为后续太阳能赛车能量策略的制定奠定了基础。     

模型在线辨识方法及其应用

本文提出了一种有效的非线性模型和参数在线估计方法。为了实现模型在线辨识，本文根据误差性能指标，给出了模型判据及计算式。根据递推加权最小二乘算法和优选判据，导出了模型和参数同时在线估计的有效算法。为了提高计算效率和数值稳定性，模型辨识和参数辨识均采用了Ｕ－Ｄ分解方法。新方法可用于飞行器非线性气动模型和参数的实时估计。实际应用结果表明，使用该方法可以有效地确定多项式、样条函数模型结构，参数辨识的结果满     

Mission Energy Prediction for Unmanned Ground Vehicles Using Real‐time Measurements and Prior Knowledge

A typical unmanned ground vehicle (UGV) mission can be composed of various tasks and several alternative paths. Small UGVs are typically teleoperated and rely on electric rechargeable batteries for their operations. Since each battery has limited energy storage capacity, it is essential to predict the expected mission energy requirement during the mission execution and update this prediction adaptively via real‐time performance measurements (e.g., vehicle power consumption and velocity). We propose and compare two methods in this paper. One is based on recursive least‐squares estimation built upon a UGV longitudinal dynamics model. The other is based on Bayesian estimation when prior knowledge (e.g., road average grade and operator driving style) is available. The proposed Bayesian prediction can effectively combine prior knowledge with real‐time performance measurements for adaptively updating the prediction of the mission energy requirement. Our experimental and simulation studies show that the Bayesian approach can yield more accurate predictions even with moderately imprecise prior knowledge. © 2013 Wiley Periodicals, Inc.     

基于AKF和RLS的车辆簧载质量辨识研究

实际应用中,车辆负载会随着乘客和货物的变化而发生显著改变.提出结合自适应卡尔曼滤波器(AKF)与递推最小二乘算法(RLS)进行车辆簧载质量的在线辨识.首先,采集四分之一车辆悬架的簧载振动加速度、动行程及车轮垂向加速度信号,对车辆悬架系统中的簧载质量和车轮的绝对速度进行估计,进而由遗忘因子递推最小二乘算法辨识车辆簧载质量.分析了在不同路面等级下,卡尔曼滤波器的过程噪声协方差和测量噪声协方差对悬架状态估计精度的影响.仿真结果显示,在选取与车辆行驶路面等级匹配的过程噪声协方差和测量噪声协方差时,车辆悬架状态参数的估计精度较高,并能够在线准确地辨识得到车辆的簧载质量值.     

Identification of Hammerstein nonlinear ARMAX systems

Two identification algorithms, an iterative least-squares and a recursive least-squares, are developed for Hammerstein nonlinear systems with memoryless nonlinear blocks and linear dynamical blocks described by ARMAX/CARMA models. The basic idea is to replace unmeasurable noise terms in the information vectors by their estimates, and to compute the noise estimates based on the obtained parameter estimates. Convergence properties of the recursive algorithm in the stochastic framework show that the parameter estimation error consistently converges to zero under the generalized persistent excitation condition. The simulation results validate the algorithms proposed.     

Vehicle Dynamics Modeling and Electronic Stability Program/ Active Front Steering Sliding Mode Integrated Control

Chassis integrated control can significantly improve vehicle handling stability and comfort. Because of the complexity of the problem, it has attracted significant research attention. We built a vehicle nonlinear dynamic model with multi‐degree freedom, including body movement, wheel movement, and electronically controlled hydraulic power steering system. We compared the magic formula tire model, Dugoff tire model, brush tire model, and LuGre dynamic friction tire model and steady model. The precision of the model was verified by a comparison between simulation results and the real vehicle test results. Then, based on the vehicle dynamics model, an AFS (active front steering) controller was designed based on sliding mode variable structure control, and an AFS and ESP (electronic stability program) integrated coordination controller was proposed. Finally, based on the nonlinear tire model and multi‐DOF (degree of freedom) vehicle model, sinusoidal and step steering angle input simulation analysis was proposed on different road friction coefficients. The results show that the vehicle has better response characteristics with coordinated control strategy when compared with AFS and ESP only control. The evidence suggests that the proposed integrated control system in this paper can improve vehicle stability and safety.     

基于计算机视觉和图像处理的交通参数检测

本文提出了一种基于计算机视觉和图像处理的车流量检测方法,通过分析CCD TV 摄 像机获取的图像中车辆和场景信息来有效地检测交通参数．本方法可以完成双向四车道高速 公路和城市道路的车辆计数和车辆速度检测．实验结果表明：车辆计数的正确率为94％,车 辆速度检测的正确率达92%,并具有满意的实时特性．     

基于递推最小二乘估计的动力学模型参数识别

动力学建模过程中,模型参数的不确定性和参数的未知变化使得建模精度大为降低。通过对非线性最小二乘估计理论的研究,提出动力学模型参数的最小二乘估计,并设计出相应的估计器,基于现场数据给予模型较为准确的定论。应用于曲柄滑块机构中滑块与滑动面间的摩擦系数的估计,经过较少的迭代次数获得满意的估计效果。为动力学模型参数识别,建立更精确模型提供一个有效的途径。     

Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems

This article studies the identification problem of the nonlinear sandwich systems. For the sandwich system, because there are inner variables which cannot be measured in the information vector of the identification models, it is difficult to identify the nonlinear sandwich systems. In order to overcome the difficulty, an auxiliary model is built to predict the estimates of inner variables by means of the output of the auxiliary model. For the purpose of employing the real‐time observed data, a cost function with dynamical data is constructed to capture on‐line information of the nonlinear sandwich system. On this basis, an auxiliary model stochastic gradient identification approach is proposed based on the gradient optimization. Moreover, an auxiliary model multiinnovation stochastic gradient estimation method is developed, which tends to enhance estimation accuracy by introducing more observed data dynamically. The numerical simulation is provided and the simulation results show that the proposed auxiliary model identification method is effective for the nonlinear sandwich systems.     

Application of LQ-based semi-active suspension control in a vehicle

A linear quadratic (LQ) based controller and observer concept for a semi-active full-car model is implemented and compared to a skyhook controller using a real vehicle in this paper. Especially an observer including a new modified road model is presented, which reliably filters low frequency disturbances induced by ascending or descending steep hills. Furthermore, methods for the parametrization of the quadratic cost function are presented and it is shown in experiments for different vehicle masses that ride comfort and road holding can be significantly improved by using semi-active suspension control. In order to take nonlinear component characteristics or suspension friction into account a parametrized nonlinear full-car model is presented, which is used for the determination of the controller cost function weights. The performance evaluation is done using a 4-poster test rig as well as measurements conducted on a real road.     

面向无人驾驶地图更新的安全信任管理方案

地图的实时性和准确性在无人驾驶车辆导航中发挥着重要作用。基于卫星图像的数字地图能够保证地图中道路信息的准确性,但是根据相关路况信息进行实时更新时,由于车辆用户数较多且来源复杂,因此无法确保数据的安全性和可靠性。提出一种基于区块链的认证与信任管理方案。利用无证书签名技术实现车辆的匿名认证,保证车辆身份的可验证性和不可否认性。设计基于区块链的信任管理机制,通过存储在区块链上的历史信任值计算路况消息的可信度,同时将可靠的路况信息发送给地图更新服务器,根据车辆提供信息的真实与否更新车辆信誉值,实现车辆信誉的同步与更新。从计算开销、通信开销和信任管理功能等方面验证了该方案的有效性。仿真结果表明,该方案具有较高的地图更新效率和防骗能力,能够确保数据来源的安全性和可靠性,为无人驾驶车辆提供实时路况更新服务。     

Vehicle state estimation for anti-lock control with nonlinear observer

Vehicle state estimation during anti-lock braking is considered. A novel nonlinear observer based on a vehicle dynamics model and a simplified Pacejka tire model is introduced in order to provide estimates of longitudinal and lateral vehicle velocities and the tire-road friction coefficient for vehicle safety control systems, specifically anti-lock braking control. The approach differs from previous work on vehicle state estimation in two main respects. The first is the introduction of a switched nonlinear observer in order to deal with the fact that in some driving situations the information provided by the sensor is not sufficient to carry out state estimation (i.e., not all states are observable). This is shown through an observability analysis. The second contribution is the introduction of tire-road friction estimation depending on vehicle longitudinal motion. Stability properties of the observer are analyzed using a Lyapunov function based method. Practical applicability of the proposed nonlinear observer is shown by means of experimental results.     

Estimation of vehicle sideslip, tire force and wheel cornering stiffness

This paper presents a process for the estimation of tire–road forces, vehicle sideslip angle and wheel cornering stiffness. The method uses measurements (yaw rate, longitudinal/lateral accelerations, steering angle and angular wheel velocities) only from sensors which can be integrated or have already been integrated in modern cars. The estimation process is based on two blocks in series: the first block contains a sliding-mode observer whose principal role is to calculate tire–road forces, while in the second block an extended Kalman filter estimates sideslip angle and cornering stiffness. More specifically, this study proposes an adaptive tire-force model that takes variations in road friction into account. The paper also presents a study of convergence for the sliding-mode observer. The estimation process was applied and compared to real experimental data, in particular wheel force measurements. The vehicle mass is assumed to be known. Experimental results show the accuracy and potential of the estimation process.     

Identification of non-linear stochastic systems by state dependent parameter estimation

The paper outlines how improved estimates of time variable parameters in models of stochastic dynamic systems can be obtained using recursive filtering and fixed interval smoothing techniques, with the associated hyper-parameters optimized by maximum likelihood based on prediction error decomposition. It then shows how, by exploiting special data re-ordering and back-fitting procedures, similar recursive parameter estimation techniques can be utilized to estimate much more rapid State Dependent Parameter (SDP) variations. In this manner, it is possible to identify and estimate a widely applicable class of nonlinear stochastic systems, as illustrated by several examples that include simulated and real data from chaotic processes. Finally, the paper points out that such SDP models can form the basis for new methods of signal processing, automatic control and state estimation for nonlinear stochastic systems.     

双参卷积理论模型预测交通通行时间

随着城市经济的发展和人们生活节奏的加快,智慧交通领域针对出行时间的研究已经成为热点问题。出行前预估行程中的通行时间便于人们更合理地规划出行路径,基于时间状态特征的路径规划就是解决交通问题的重要手段之一。现有模型多关注于车辆到达时间或多结合于真实历史时间数据进行预测,对浮动车的运行状态、车速等是否对时间存在影响的问题研究较少。基于此现状,提出了一种基于状态特征的道路时间预测模型,在固定时段内,利用出租车载客与否情况对轨迹数据进行深度相关性分析,结合车辆行驶速度构建一个基于密度划分的双参卷积理论模型,用得到的最终速度值对通行时间进行计算。实验结果表明该模型算法与传统时间预测算法相比有更高的精确度和实用性,提高了人们对出行安排的合理化和层次化,对制定城市道路出行策略具有重要的意义。     

车载导航电子地图的路网模型

建立适合于车载导航系统的路网数据是车载导航系统的关键。该文描述了如何表达真实世界的交通要素,并结合实际情况给出数据模型。